Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data

Howard P. Maile; Ji Peng Olivia Li; Mary D. Fortune; Patrick Royston; Marcello T. Leucci; Ismail Moghul; Anita Szabo; Konstantinos Balaskas; Bruce D. Allan; Alison J. Hardcastle; Pirro Hysi; Nikolas Pontikos; Stephen J. Tuft; Daniel M. Gore

doi:10.1016/j.ajo.2022.04.004

Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data

Howard P. Maile, Ji Peng Olivia Li, Mary D. Fortune, Patrick Royston, Marcello T. Leucci, Ismail Moghul, Anita Szabo, Konstantinos Balaskas, Bruce D. Allan, Alison J. Hardcastle, Pirro Hysi, Nikolas Pontikos, Stephen J. Tuft, Daniel M. Gore^*

^*Corresponding author for this work

Ophthalmology

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

PURPOSE: To generate a prognostic model to predict keratoconus progression to corneal crosslinking (CXL). DESIGN: Retrospective cohort study. METHODS: We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were available. We investigated both keratometry or CXL as end points for progression and used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model. We calculated hazard ratios (HRs) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions. RESULTS: After exclusions, model fitting comprised 8701 eyes, of which 3232 underwent CXL. For early keratoconus, CXL provided a more robust prognostic model than keratometric progression. The final model explained 33% of the variation in time to event: age HR (95% CI) 0.9 (0.90-0.91), maximum anterior keratometry 1.08 (1.07-1.09), and minimum corneal thickness 0.95 (0.93-0.96) as significant covariates. Single-nucleotide polymorphisms (SNPs) associated with keratoconus (n=28) did not significantly contribute to the model. The predicted time-to-event curves closely followed the observed curves during internal-external validation. Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability. CONCLUSIONS: A prognostic model to predict keratoconus progression could aid patient empowerment, triage, and service provision. Age at presentation is the most significant predictor of progression risk. Candidate SNPs associated with keratoconus do not contribute to progression risk.

Original language	English
Pages (from-to)	321-329
Number of pages	9
Journal	American journal of ophthalmology
Volume	240
DOIs	https://doi.org/10.1016/j.ajo.2022.04.004
Publication status	Published - Aug 2022

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10.1016/j.ajo.2022.04.004

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Maile, H. P., Li, J. P. O., Fortune, M. D., Royston, P., Leucci, M. T., Moghul, I., Szabo, A., Balaskas, K., Allan, B. D., Hardcastle, A. J., Hysi, P., Pontikos, N., Tuft, S. J., & Gore, D. M. (2022). Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data. American journal of ophthalmology, 240, 321-329. https://doi.org/10.1016/j.ajo.2022.04.004

@article{a282ffc3941e42f4bc1c3a3b54ef0a9f,

title = "Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data",

abstract = "PURPOSE: To generate a prognostic model to predict keratoconus progression to corneal crosslinking (CXL). DESIGN: Retrospective cohort study. METHODS: We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were available. We investigated both keratometry or CXL as end points for progression and used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model. We calculated hazard ratios (HRs) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions. RESULTS: After exclusions, model fitting comprised 8701 eyes, of which 3232 underwent CXL. For early keratoconus, CXL provided a more robust prognostic model than keratometric progression. The final model explained 33% of the variation in time to event: age HR (95% CI) 0.9 (0.90-0.91), maximum anterior keratometry 1.08 (1.07-1.09), and minimum corneal thickness 0.95 (0.93-0.96) as significant covariates. Single-nucleotide polymorphisms (SNPs) associated with keratoconus (n=28) did not significantly contribute to the model. The predicted time-to-event curves closely followed the observed curves during internal-external validation. Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability. CONCLUSIONS: A prognostic model to predict keratoconus progression could aid patient empowerment, triage, and service provision. Age at presentation is the most significant predictor of progression risk. Candidate SNPs associated with keratoconus do not contribute to progression risk.",

author = "Maile, {Howard P.} and Li, {Ji Peng Olivia} and Fortune, {Mary D.} and Patrick Royston and Leucci, {Marcello T.} and Ismail Moghul and Anita Szabo and Konstantinos Balaskas and Allan, {Bruce D.} and Hardcastle, {Alison J.} and Pirro Hysi and Nikolas Pontikos and Tuft, {Stephen J.} and Gore, {Daniel M.}",

note = "Funding Information: Funding/Support: Howard P. Maile is funded by a Moorfields Eye Charity PhD Studentship (GR001147). Nikolas Pontikos is funded by a Moorfields Eye Charity Career Development Award (R190031A). Moorfields Eye Charity is supported in part by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. Stephen J. Tuft, Bruce D. Allan, and Daniel M. Gore acknowledge that a proportion of their financial support is from the Department of Health through the award made by the National Institute for Health Research to Moorfields Eye Hospital NHS Foundation Trust and University College London Institute of Ophthalmology for a Specialist Biomedical Research Centre for Ophthalmology. Publisher Copyright: {\textcopyright} 2022 Elsevier Inc.",

year = "2022",

month = aug,

doi = "10.1016/j.ajo.2022.04.004",

language = "English",

volume = "240",

pages = "321--329",

journal = "American journal of ophthalmology",

issn = "0002-9394",

publisher = "Elsevier",

}

Maile, HP, Li, JPO, Fortune, MD, Royston, P, Leucci, MT, Moghul, I, Szabo, A, Balaskas, K, Allan, BD, Hardcastle, AJ, Hysi, P, Pontikos, N, Tuft, SJ & Gore, DM 2022, 'Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data', American journal of ophthalmology, vol. 240, pp. 321-329. https://doi.org/10.1016/j.ajo.2022.04.004

TY - JOUR

T1 - Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data

AU - Maile, Howard P.

AU - Li, Ji Peng Olivia

AU - Fortune, Mary D.

AU - Royston, Patrick

AU - Leucci, Marcello T.

AU - Moghul, Ismail

AU - Szabo, Anita

AU - Balaskas, Konstantinos

AU - Allan, Bruce D.

AU - Hardcastle, Alison J.

AU - Hysi, Pirro

AU - Pontikos, Nikolas

AU - Tuft, Stephen J.

AU - Gore, Daniel M.

N1 - Funding Information: Funding/Support: Howard P. Maile is funded by a Moorfields Eye Charity PhD Studentship (GR001147). Nikolas Pontikos is funded by a Moorfields Eye Charity Career Development Award (R190031A). Moorfields Eye Charity is supported in part by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. Stephen J. Tuft, Bruce D. Allan, and Daniel M. Gore acknowledge that a proportion of their financial support is from the Department of Health through the award made by the National Institute for Health Research to Moorfields Eye Hospital NHS Foundation Trust and University College London Institute of Ophthalmology for a Specialist Biomedical Research Centre for Ophthalmology. Publisher Copyright: © 2022 Elsevier Inc.

PY - 2022/8

Y1 - 2022/8

N2 - PURPOSE: To generate a prognostic model to predict keratoconus progression to corneal crosslinking (CXL). DESIGN: Retrospective cohort study. METHODS: We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were available. We investigated both keratometry or CXL as end points for progression and used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model. We calculated hazard ratios (HRs) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions. RESULTS: After exclusions, model fitting comprised 8701 eyes, of which 3232 underwent CXL. For early keratoconus, CXL provided a more robust prognostic model than keratometric progression. The final model explained 33% of the variation in time to event: age HR (95% CI) 0.9 (0.90-0.91), maximum anterior keratometry 1.08 (1.07-1.09), and minimum corneal thickness 0.95 (0.93-0.96) as significant covariates. Single-nucleotide polymorphisms (SNPs) associated with keratoconus (n=28) did not significantly contribute to the model. The predicted time-to-event curves closely followed the observed curves during internal-external validation. Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability. CONCLUSIONS: A prognostic model to predict keratoconus progression could aid patient empowerment, triage, and service provision. Age at presentation is the most significant predictor of progression risk. Candidate SNPs associated with keratoconus do not contribute to progression risk.

AB - PURPOSE: To generate a prognostic model to predict keratoconus progression to corneal crosslinking (CXL). DESIGN: Retrospective cohort study. METHODS: We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were available. We investigated both keratometry or CXL as end points for progression and used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model. We calculated hazard ratios (HRs) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions. RESULTS: After exclusions, model fitting comprised 8701 eyes, of which 3232 underwent CXL. For early keratoconus, CXL provided a more robust prognostic model than keratometric progression. The final model explained 33% of the variation in time to event: age HR (95% CI) 0.9 (0.90-0.91), maximum anterior keratometry 1.08 (1.07-1.09), and minimum corneal thickness 0.95 (0.93-0.96) as significant covariates. Single-nucleotide polymorphisms (SNPs) associated with keratoconus (n=28) did not significantly contribute to the model. The predicted time-to-event curves closely followed the observed curves during internal-external validation. Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability. CONCLUSIONS: A prognostic model to predict keratoconus progression could aid patient empowerment, triage, and service provision. Age at presentation is the most significant predictor of progression risk. Candidate SNPs associated with keratoconus do not contribute to progression risk.

UR - http://www.scopus.com/inward/record.url?scp=85131383999&partnerID=8YFLogxK

U2 - 10.1016/j.ajo.2022.04.004

DO - 10.1016/j.ajo.2022.04.004

M3 - Article

C2 - 35469790

AN - SCOPUS:85131383999

SN - 0002-9394

VL - 240

SP - 321

EP - 329

JO - American journal of ophthalmology

JF - American journal of ophthalmology

ER -

Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data

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